Energy harvesting techniques have become a crucial area of research due to the significant development of Internet of Things and low-power electronics. The major limitations of these energy harvesting circuits are low efficiency and low output voltage level. This paper presents a self-solar-powered boost converter circuit to improve the efficiency and output voltage level simultaneously. The proposed circuit is fabricated and provides 88.92% maximum efficiency and 49.8 V output voltage at 5.6 V solar input voltage for 10 kΩ load resistance. The maximum output power is obtained 247 mW at an input power of 280.56 mW. The experimental output dataset has been classified into three different clusters using R programming. The dataset of output power levels is divided into low, high, and moderate levels. K-means has been carried out based on different experimental output data using simulation. The predicted values of different output parameters also have been carried. A linear equation has been developed to predict the output parameters using linear regression method.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Experimental Performance Analysis and Prediction of Output of Solar-Powered Boost Converter Circuit by Varying Load

  • Joydeep Banerjee,
  • Subhasish Banerjee,
  • Sushmita Bandyopadhyay,
  • Kalyan Biswas

摘要

Energy harvesting techniques have become a crucial area of research due to the significant development of Internet of Things and low-power electronics. The major limitations of these energy harvesting circuits are low efficiency and low output voltage level. This paper presents a self-solar-powered boost converter circuit to improve the efficiency and output voltage level simultaneously. The proposed circuit is fabricated and provides 88.92% maximum efficiency and 49.8 V output voltage at 5.6 V solar input voltage for 10 kΩ load resistance. The maximum output power is obtained 247 mW at an input power of 280.56 mW. The experimental output dataset has been classified into three different clusters using R programming. The dataset of output power levels is divided into low, high, and moderate levels. K-means has been carried out based on different experimental output data using simulation. The predicted values of different output parameters also have been carried. A linear equation has been developed to predict the output parameters using linear regression method.